There Is No Preview Available For This Item
This item does not appear to have any files that can be experienced on Archive.org.
Please download files in this item to interact with them on your computer.
Show all files
In this talk I will describe two design problems in areas of chemistry and computer science which yield themselves to machine learning techniques.
The area of supra-molecular chemistry deals with mechanisms of noncovalent assembly of particles. The interest in understanding the underlying mechanisms is two-fold: it provides insight into protein complex formation and paves the way for application of these mechanisms in nanotechnology. The spontaneous assembly of particles is referred to as self-assembly and the ability to design such processes holds great promise for nanotechnology. The problem of design of self-assembly processes turns out to be a task surprisingly familiar to the machine learning community: that of probability maximization. The standard Boltzmann machine learning rule can be applied to this task, and I will demonstrate a way to speed up the evaluation of the derivatives via importance sampling. In addition, I will demonstrate several methods for evaluating the probability of a shape under a self-assembly process.
The second part of my talk will be devoted to another problem which at first blush does not admit a probabilistic interpretation. This is the problem of inferring programs given input/output pairs. I will introduce a probabilistic representation of code. This representation gives rise to a distribution of state sequences and allows approximate inference in form of loopy belief propagation. I will illustrate the performance of this method on tasks of discovering programs for polynomial computation and list reversal given only examples of the input/output pairs.